\documentclass{article}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{color}
\usepackage{colortbl}
\usepackage{multirow}
\usepackage{float}
\usepackage{makecell}
\usepackage{setspace}
\usepackage[margin=1in]{geometry}
\usepackage{blindtext}

\title{Appendix}
\date{}


\begin{document}
\maketitle



<<echo=FALSE,include=FALSE >>=
library(knitr)
library(tidyverse)
library(stargazer)
library(xtable)
library(kableExtra)
library(psych)
library(haven)
library(estimatr)



#df <- import data24.csv
#dt <- #import data12.csv
opts_chunk$set(echo=FALSE,
               message=FALSE,
               warning=FALSE)


wtd.describe <- function(x, weights=NULL, trim=.1){
      require(TAM)
      require(diagis)
      require(robsurvey)
      out <- NULL
      # Handling simple vectors
      x <- as.data.frame(x)
      # If no weights given, all weights = 1
      if(is.null(weights)) {weights <- seq(1, nrow(x))}
      i <- 1
      for(colname in colnames(x)){
        # Removing rows with missing data or weight
        d <- x[complete.cases(x[[colname]], weights), , drop=FALSE][[colname]]
        w <- weights[complete.cases(x[[colname]], weights)]
        wd <- data.frame(
          "vars"     = i,
          "n"        = length(d),
          "mean"     = TAM::weighted_mean(d, w = w),
          "sd"       = TAM::weighted_sd(d, w = w),
          "median"   = robsurvey::weighted_median(d, w = w, na.rm = TRUE),
          "min"      = min(d),
          "max"      = max(d),
          "range"    = max(d) - min(d),
          row.names  = colname
        )
        i <- i+1
        out <- rbind(out, wd)
      }
      return(out)
    }

@


The appendix contains the descriptive statistics and statistical models from the 2012 State of Washington and 2024 YouGov surveys. Tables one through four are the counts of court and police experiences and evaluations for all respondents and by race. The 2012 counts are respondents’ perceptions of being treated rudely or unfairly by the courts or the police. The 2024 counts are also perceptions of experiences with additional differentiation of positive and negative experiences. Variables related to Courts and Police for 2012 are shown in table three, and covariates included in our models are in Table four. Tables five through and eight show the unweighted and weighted court evaluations, experiences and the covariates of interest. Because the survey oversamples racial groups, we ran a weighted least regression to show results that were in line with the population of Washington. Table five shows the unweighted mean and standard deviations of court and police variables which you can compare to the weighted versions of those variables in Table 6.  \par

In the 2024 survey, police and court evaluations are feeling thermometer ratings of "Police in your community" and "Judges and court officials in your community". The 2024 data does not ask respondents to differentiate rude or unfair experiences but asks them how many experiences they had in the last five years where a police officer/judges or court officials treated them positively (“treated you politely, treated you fairly, (was/were) helpful to you, or performed effectively”) or negatively (treated you impolitely, treated you unfairly, (was/were) not helpful to you or performed ineffectively”). Tables nine and ten show the descriptive statistics for the 2024 YouGov survey. Tables eleven and twelve show the counts for the indicators of most memorable experiences and counts for no experience. The remainder of the appendix contains the models included in the main document, except Table 14, which shows the modeling of 2012 without survey weights. \par
\newpage
% tab 1
<<>>=

all<- dt%>%   
      dplyr::select(c_exp,p_exp)%>%
      describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x<-c("white","black","hispanic","asian")
x1<-data.frame()
x2<-data.frame()
for(i in 1:length(x)){
 x1<- dt%>%
    .[.[[x[i]]]==1,]%>%
    dplyr::select(c_exp,p_exp)%>%
     describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x2<-rbind.data.frame(x2,x1)}
x2<-rbind.data.frame(x2,all)
rm(all)
x2 %>% `rownames<-`(c("Court experiences (White)",
                      "Police experiences (White)",
                      "Court experiences (Black)",
                      "Police experiences (Black)",
                      "Court experiences (Hispanic)",
                      "Police experiences (Hispanic)",
                      "Court experiences (Asian)",
                      "Police experiences (Asian)",
                      "Court experiences (All)",
                      "Police experiences (All)"
               ))%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police and Court count of experiences all respondents and by race (2012)")%>%
kable_styling(latex_options = c("striped","hold_position"))

@

% tab 2
<<>>=

all<- dt%>%   
      dplyr::select(c_eval,p_eval)%>%
      describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x<-c("white","black","hispanic","asian")
x1<-data.frame()
x2<-data.frame()
for(i in 1:length(x)){
 x1<- dt%>%
    .[.[[x[i]]]==1,]%>%
    dplyr::select(c_eval,p_eval)%>%
     describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x2<-rbind.data.frame(x2,x1)}
x2<-rbind.data.frame(x2,all)
rm(all)
x2 %>% `rownames<-`(c("Court  evaluations (White)",
                      "Police evaluations (White)",
                      "Court  evaluations (Black)",
                      "Police evaluations (Black)",
                      "Court  evaluations (Hispanic)",
                      "Police evaluations (Hispanic)",
                      "Court  evaluations (Asian)",
                      "Police evaluations (Asian)",
                      "Court  evaluations (All)",
                      "Police evaluations (All)"
               ))%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police and Court evaluations all respondents and by race (2012)")%>%
kable_styling(latex_options = c("striped","hold_position"))

@


% tab 3
<<>>=

all<-df%>%
    dplyr::select(cneg,cpos,pneg,ppos)%>%
     describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x<-c("white_nh","black","hispanic")
x1<-data.frame()
x2<-data.frame()
for(i in 1:length(x)){
 x1<- df%>%
    .[.[[x[i]]]==1,]%>%
    dplyr::select(cneg,cpos,pneg,ppos)%>%
     describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x2<-rbind.data.frame(x2,x1)}

x2<-rbind.data.frame(x2,all)
rm(all)

x2%>%
`rownames<-`(c("Court (neg) experiences (White)",
               "Court (pos) experiences (White)",
               "Police (neg) experiences (White)",
               "Police (pos) experiences (White)",
               "Court (neg) experiences (Black)",
               "Court (pos) experiences (Black)",
               "Police (neg) experiences (Black)",
               "Police (pos) experiences (Black)",
               "Court (neg) experiences (Hispanic)",
               "Court (pos) experiences (Hispanic)",
               "Police (neg) experiences (Hispanic)",
               "Police (pos) experiences (Hispanic)",
               "Court (neg) experiences (All)",
               "Court (pos) experiences (All)",
               "Police (neg) experiences (All)",
               "Police (pos) experiences (All)"
               ))%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police and Court count of experiences all respondents and by race (2024)")%>%
kable_styling(latex_options = c(full_width = TRUE,"striped","hold_position"))

@

% tab 4
<<>>=

all<-df%>%
    dplyr::select(ftjudcourt,ftpolice)%>%
     describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x<-c("white_nh","black","hispanic")
x1<-data.frame()
x2<-data.frame()
for(i in 1:length(x)){
 x1<- df%>%
    .[.[[x[i]]]==1,]%>%
    dplyr::select(ftjudcourt,ftpolice)%>%
     describe(.)%>%
    .[,c(2,3,4,5,8,9,10)]

x2<-rbind.data.frame(x2,x1)}

x2<-rbind.data.frame(x2,all)
rm(all)

x2%>%
`rownames<-`(c("Court (FT)  evaluations (White)",
               "Police (FT) evaluations (White)",
               "Court (FT)  evaluations (Black)",
               "Police (FT) evaluations (Black)",
               "Court (FT)  evaluations (Hispanic)",
               "Police (FT) evaluations (Hispanic)",
               "Court (FT)  evaluations (All)",
               "Police (FT) evaluations (All)"))%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police and Court count of experiences all respondents and by race (2024)")%>%
kable_styling(latex_options = c(full_width = TRUE,"striped","hold_position"))

@



% tab 5
<<>>=

dt%>%
dplyr::select(c_eval,p_eval,c_exp,p_exp)%>%
`colnames<-`(c("Court evaluation","Police evaluation","Court experiences","Police experiences"))%>%
describe(.)%>%
.[,c(2,3,4,5,8,9,10)]%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police and Court (2012)")%>%
kable_styling(latex_options = c("striped","hold_position"))

@





% tab 6
<<>>=

dt%>%
dplyr::select(c_eval,p_eval,c_exp,p_exp,weight)%>%
`colnames<-`(c("Court evaluation","Police evaluation","Court experiences","Police experiences","weight"))%>%
wtd.describe(.,weights=.[["weight"]])%>%
.[-c(5),c(2,3,4,5,6,7,8)]%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Weighted Police and Court variables (2012)")%>%
  kable_styling(latex_options = c("striped","hold_position"))



@


% tab 7
<<>>=

dt%>%
dplyr::select(female,pidc,pid_dk,black,hispanic,asian,educ,age)%>%
`colnames<-`(c("Female","Partisan identity","Partisan (don't know)","Black"
,"Hispanic","Asian","Education","Age"))%>%
describe(.)%>%
.[,c(2,3,4,5,8,9,10)]%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Covariates (2012)")%>%
  kable_styling(latex_options = c("striped","hold_position"))

@

% tab 8
<<>>=

dt%>%
dplyr::select(female,pidc,pid_dk,black,hispanic,asian,educ,age,weight)%>%
`colnames<-`(c("Female","Partisan identity","Partisan (don't know)","Black"
,"Hispanic","Asian","Education","Age","weight"))%>%
wtd.describe(.,weights=.[["weight"]])%>%
.[-c(9),c(2,3,4,5,6,7,8)]%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="weighted covariates (2012)")%>%
  kable_styling(latex_options = c("striped","hold_position"))



@



% tab 9
<<>>=

df%>%
dplyr::select(ftjudcourt,ftpolice,cneg,cpos,pneg,ppos,mem_crt,mem_pol,cne,pne)%>%
`colnames<-`(c("Court evaluation (FT)","Police evaluation (FT)","Court negative experiences","Court positive experiences","Police negative experiences","Police positive experiences","Court most memorable","Police most memorable","Court no experience","Police no experience"))%>%
describe(.)%>%
.[,c(2,3,4,5,8,9,10)]%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police and Court (2024)")%>%
kable_styling(latex_options = c("striped","Hold_position"))

@

% tab 10
<<>>=

df%>%
dplyr::select(female,pidc,pid_dk,black,hispanic,educ,age,neg_aff,nat_ft_avg)%>%
`colnames<-`(c("Female","Partisan identity","Partisan (don't know)","Black"
,"Hispanic","Education","Age","Negative affect","Avg. feeling thermometer rating"))%>%
describe(.)%>%
.[,c(2,3,4,5,8,9,10)]%>%
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption=" Covariates (2024)")%>%
  kable_styling(latex_options = c("striped","hold_position"))

@

% tab 11
<<>>=

df%>%
dplyr::select(PVN,PSN,PN,PSP,PVP,PNE)%>%
`colnames<-`(c( "Police very negative",
                "Police somewhat negative",
                "Police neutral",
                "Police somewhat positive",
                "Police very positive",
                "Police no experience"))%>%
describe(.)%>%
.[,c(3,4)]%>%
mutate(n=c(table(df$PVN)[[2]],table(df$PSN)[[2]],table(df$PN)[[2]],table(df$PSP)[[2]],table(df$PVP)[[2]],table(df$PNE)[[2]]))%>%
.[,c(3,1)]%>% 
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Police Indicators for most memorable experience")%>%
  kable_styling(latex_options = c("striped","hold_position"))

@

% tab 12
<<>>=

df%>%
dplyr::select(CVN,CSN,CN,CSP,CVP,CNE)%>%
`colnames<-`(c( "Court very negative",
                "Court somewhat negative",
                "Court neutral",
                "Court somewhat positive",
                "Court very positive",
                "Court no experience"))%>%
describe(.)%>%
.[,c(3,4)]%>%
mutate(n=c(table(df$CVN)[[2]],table(df$CSN)[[2]],table(df$CN)[[2]],table(df$CSP)[[2]],table(df$CVP)[[2]],table(df$CNE)[[2]]))%>%
.[,c(3,1)]%>% 
kable(.,format="latex",digits=2,booktabs=TRUE,linesep = "",caption="Court Indicators for most memorable experience")%>%
  kable_styling(latex_options = c("striped","hold_position"))

@



% tab 13
\begin{table}
<<results='asis'>>=
list(
 lm(c_eval~c_exp+p_exp+female+educ+pidc+pid_dk+age+black+hispanic+asian,weights = weight,data=dt),
 lm(p_eval~c_exp+p_exp+female+educ+pidc+pid_dk+age+black+hispanic+asian,weights = weight,data=dt))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            dep.var.labels = c("",""),
            dep.var.caption=c("2012"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court experiences",
                "Police experiences",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Asian",
                "Intercept"))


@
\caption{Models correspond to Figure 1 in main document}
\end{table}

% tab 14
\begin{table}
<<results='asis'>>=
list(
 lm(c_eval~c_exp+p_exp+female+educ+pidc+pid_dk+age+black+hispanic+asian,data=dt),
 lm(p_eval~c_exp+p_exp+female+educ+pidc+pid_dk+age+black+hispanic+asian,data=dt))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            dep.var.labels = c("",""),
            #se = starprep(.,stat="std.error", se_type = "HC2"),
            dep.var.caption=c("2012"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court experiences",
                "Police experiences",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Asian",
                "Intercept"))


@
\caption{Models correspond to Figure 1 in main document but without survey weights}
\end{table}

% tab 15
\begin{table}
<<results='asis'>>=

 list(
  lm(ftjudcourt~cneg+pneg+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
  lm(ftpolice~cneg+pneg+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            #se = starprep(.,stat="std.error", se_type = "HC2"),
            dep.var.labels = c("",""),
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court negative",
                "Police negative",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))

@
\caption{Models correspond to panel A and C of Figure 2 in main document}
\end{table}

% tab 16
\begin{table}
<<results='asis'>>=

 list(
  lm(ftjudcourt~cneg+pneg+pneg*pnc1+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(ftpolice~cneg+pneg+cneg*cnp1+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
           # se = starprep(.,stat="std.error", se_type = "HC2"),
            #dep.var.labels = c("",""),
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court negative",
                "Police negative",
                "Police only",
                "Court only",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Police negative X Police only",
                "Court negative X Court only",
                "Intercept"))

@
\caption{Models correspond to panel B. and D. of Figure 2 in main document}
\end{table}


\begin{table}
<<results='asis'>>=

 list(
  lm(ftjudcourt~cneg+pneg+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
  lm(ftpolice~cneg+pneg+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            #se = starprep(.,stat="std.error", se_type = "HC2"),
            dep.var.labels = c("",""),
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court negative",
                "Police negative",
                "Negative affect",
                "Average feeling thermometer",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))

@
\caption{Models correspond to panel A and C of Figure 3 in main document}
\end{table}

% tab 16
\begin{table}
<<results='asis'>>=

 list(
  lm(ftjudcourt~cneg+pneg+pneg*pnc1+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(ftpolice~cneg+pneg+cneg*cnp1+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
           # se = starprep(.,stat="std.error", se_type = "HC2"),
            #dep.var.labels = c("",""),
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court negative",
                "Police negative",
                "Police only",
                "Court only",
                "Negative affect",
                "Average feeling thermometer",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Police negative X Police only",
                "Court negative X Court only",
                "Intercept"))

@
\caption{Models correspond to panel B. and D. of Figure 3 in main document}
\end{table}


% tab 18
\begin{table}
<<results='asis'>>=

 list(
  lm(ftjudcourt~cneg+cpos+pneg+ppos+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(ftpolice~cneg+cpos+pneg+ppos+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court negative",
                "Court positive",
                "Police negative",
                "Police positive",
                "Negative affect",
                "Average feeling thermometer",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))

@
\caption{Models correspond to Figure 4 in main document}
\end{table}

% tab 19
\begin{table}
<<results='asis'>>=

 list(
  lm(ftjudcourt~mem_crt+mem_pol+cne+pne+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(ftpolice~mem_crt+mem_pol+cne+pne+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Memorable Court",
                "Memorable Police",
                "No court experience",
                "No police experience",
                "Negative affect",
                "Average feeling thermometer",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))

@
\caption{Models correspond to the top row of Figure 5 in main document}
\end{table}


% tab 20
\begin{table}
<<results='asis'>>=

df$p_mem<-factor(df$mem_pol,levels=c(-2,-1,0,1,2),labels = c("Police very negative","Police somewhat negative","Police neutral","Police somewhat positive","Police very positive"))
df$c_mem<-factor(df$mem_crt,levels=c(-2,-1,0,1,2),labels = c("Court very negative","Court somewhat negative","Court neutral","Court somewhat positive","Court very positive"))
df$p_mem<-relevel(df$p_mem,ref = "Police neutral")
df$c_mem<-relevel(df$c_mem,ref = "Court neutral")


 list(
  lm(ftjudcourt~CVN+CSN+CN+CSP+CVP+PVN+PSN+PN+PSP+PVP+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(ftpolice~CVN+CSN+CN+CSP+CVP+PVN+PSN+PN+PSP+PVP+neg_aff+nat_ft_avg+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Court Evaluations","Police Evaluations"),
            covariate.labels = c(
                "Court very negative",
                "Court somewhat negative",
                "Court neutral",
                "Court somewhat positive",
                "Court very positive",
                "Police very negative",
                "Police somewhat negative",
                "Police neutral",
                "Police somewhat positive",
                "Police very positive",
                "Negative affect",
                "Average feeling thermometer",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))

@
\caption{Models correspond to the bottom row of Figure 5 in main document}
\end{table}



\begin{table}
<<results='asis'>>=

  list(
  lm(ftbiden~  dr_avg+cneg+cpos+pneg+ppos+neg_aff+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(fttrump~dr_avg+cneg+cpos+pneg+ppos+neg_aff+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            #se = starprep(.,stat="std.error", se_type = "HC2"),
            #dep.var.labels = c("",""),
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Biden FT","Trump FT"),
            covariate.labels = c(
                "Avg. Dem \\& Rep FT",
                "Court negative",
                "Court positive",
                "Police negative",
                "Police positive",
                "Negative affect",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))
 
@
 \caption{Placebo test of Biden and Trump feeling thermometers}
\end{table}
 
\begin{table}
<<results='asis'>>= 
   
 
 list(
  lm(ftdemcon~ bt_avg+cneg+cpos+pneg+ppos+neg_aff+female+educ+pidc+pid_dk+age+black+hispanic,data=df),
    lm(ftrepcon~bt_avg+cneg+cpos+pneg+ppos+neg_aff+female+educ+pidc+pid_dk+age+black+hispanic,data=df))%>%
  stargazer(.,
            type="latex",
            style="ajps",
            digits=2,
            float=FALSE,
            model.numbers = FALSE,
            column.sep.width = "3pt",
            #se = starprep(.,stat="std.error", se_type = "HC2"),
            #dep.var.labels = c("",""),
            dep.var.labels.include = FALSE,
            dep.var.caption=c("2024"),
            column.labels = c("Democrat FT","Republican FT"),
            covariate.labels = c(
                "Avg. Biden \\& Trump FT",
                "Court negative",
                "Court positive",
                "Police negative",
                "Police positive",
                "Negative affect",
                "Female",
                "Education",
                "Partisan identity",
                "Partisan (don't know)",
                "Age",
                "Black",
                "Hispanic",
                "Intercept"))
 
@
\caption{Placebo test of party feeling thermometers}
\end{table}

\end{document}
